Conceptual schema of miRNA's expression: Using efficient information systems practices to manage and analyse data about miRNA expression studies in breast cancer

Burriel, Verónica; Pastor, Oscar; Peña‐Chilet, María; Martínez, María Teresa; Ribas, Gloría

doi:10.1109/rcis.2016.7549371

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…This shows that the noise information is indeed less after feature optimization, and the new features can more accurately describe the visual characteristics of the image. (3) The overfitting of the model is alleviated to a certain extent. The number of samples in the breast dataset is small and unbalanced, and many original features are overfitted.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The scarcity of medical image samples can easily lead to fitting of the recognition model. In summary, how to deal with the shortage of medical image samples has become particularly important [ 2 , 3 ]. In response to this problem, some scholars proposed to use the GAN (generative adversarial networks) model to generate new samples to expand the dataset, but the authenticity of the new selections was questioned; some scholars built a multitask learning framework (such as compound segmentation and recognition), that is, to deal with the scarcity of samples through information sharing between different tasks.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection

Bhende¹,

Thakare²,

Pant

et al. 2022

BioMed Research International

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Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization. The multiview effective region gene optimization (MvERGS) algorithm is designed to refine the original image features, improve the feature discriminate and compress the feature dimension, better match the number of samples, and perform discriminate correlation analysis (DCA) on the advanced new features; in-depth cross-modal correlation between heterogeneous elements, that is, the deep pathological information, can be mined to describe the breast mass lesion area accurately. Based on deep pathological information and traditional classifiers, an efficient breast mass recognition model is trained to complete the classification of mammography images. Experiments show that the key technical indicators of the recognition model, including accuracy and AUC, are better than the mainstream baselines, and the overfitting problem caused by the scarcity of samples is alleviated.

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Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection

Bhende¹,

Thakare²,

Pant

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…At present, the classification methods based on mammography images are demonstrated as follows: (1) Feng et al proposed a method for detecting lesions based on region growth [ 4 ]; (2) Hmaidan et al used Z-moments as the shape which is one of the methods of a shape descriptor [ 5 ]; (3) Orel et al adopted the salient index number to represent the geometry of the lesion boundary [ 6 ]; (4) Burriel et al adopted the strategy of convolutional neural network (CNN) segmentation in the feature extraction stage [ 7 ]; and (5) detection and classification by wavelet transform were proposed by researchers and highlight the different methods of mammography.…”

Section: Introductionmentioning

confidence: 99%

Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification

Mann¹,

Bindal²,

Balyan³

et al. 2022

BioMed Research International

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Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches.

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Assessing Breast Cancer Risk with 5G-Enabled Data-Sensorized Sample using Random Forest

Galeas-Cutiño,

Soto,

Zamorano-Illanes

et al. 2023

2023 South American Conference on Visible Light Communications (SACVLC)

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Conceptual schema of miRNA's expression: Using efficient information systems practices to manage and analyse data about miRNA expression studies in breast cancer

Cited by 4 publications

References 2 publications

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection

Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification

Assessing Breast Cancer Risk with 5G-Enabled Data-Sensorized Sample using Random Forest

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